Reduced-Dimension Robust Capon Beamforming Using Krylov-Subspace Techniques

被引：39
作者：
Somasundaram, Samuel D. [1 ]
Parsons, Nigel H. [1 ]
Li, Peng [2 ,4 ]
De Lamare, Rodrigo C. [3 ,4 ]
机构：
[1] Thales UK Ltd, Gen Sonar Studies, Stockport SK3 0XB, Cheshire, England
[2] Pontificia Univ Catolica Rio de Janeiro, Rio de Janeiro, Brazil
[3] Pontificia Univ Catolica Rio de Janeiro, Ctr Telecommun Studies CETUC, Rio de Janeiro, Brazil
[4] Univ York, Dept Elect, Commun Res Grp, York YO10 5DD, N Yorkshire, England
来源：
IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS | 2015年 / 51卷 / 01期
关键词：
CONJUGATE-GRADIENT TECHNIQUES; INTERFERENCE SUPPRESSION; DERIVATIVE CONSTRAINTS; PARAMETER-ESTIMATION; ALGORITHMS; PROJECTION; EQUALIZATION; PERFORMANCE;
D O I：
10.1109/TAES.2014.130485
中图分类号：
V [航空、航天];
学科分类号：
08 ; 0825 ;
摘要：
We present low-complexity, quickly converging robust adaptive beamformers, for beamforming large arrays in snapshot deficient scenarios. The proposed algorithms are derived by combining data-dependent Krylov-subspace-based dimensionality reduction, using the Powers-of-R or conjugate gradient (CG) techniques, with ellipsoidal uncertainty set based robust Capon beamformer methods. Further, we provide a detailed computational complexity analysis and consider the efficient implementation of automatic, online dimension-selection rules. We illustrate the benefits of the proposed approaches using simulated data.
引用
收藏
页码：270 / 289
页数：20
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